Neurodegenerative and psychiatric disorders, as well as healthy aging, are all frequently associated with structural changes in the brain. These changes can cause alterations in the imaging properties of brain tissue, as well as changes in morphometric properties of the brain, such as volume, folding and surface area. This can be problematic, as analysis techniques that quantify morphometric changes without accounting for variability in the imaging properties of the tissue are liable to generate erroneous results in situations in which the tissue parameters have changed. In this grant application support is sought to construct a set of accurate and automated tools for the analysis of structural neuroimaging data. These tools will quantify alterations in brain morphometry, as well as changes in the tissue parameters that give rise to image contrast in magnetic resonance images. It is hypothesized that explicitly basing the analysis tools upon knowledge of the underlying physical principles that govern the imaging process will allow the characterization of subtle structural changes that have previously gone undetected. Aim 1 of this application is to develop a set of scans and optimization techniques that will allow for the accurate estimation of the underlying tissue parameters (i.e., T1, T2, proton density). Additional effort will be focused on removing various sources of inaccuracies in the data acquisition. This includes using optimization techniques to derive MR protocols with optimal contrast-to-noise, as well as the correction of various sources of distortion that arise in MR images such as gradient nonlinearities and real-time online correction of within-scan subject motion. This latter technique is of particular importance, as it will allow the tools to be applied to patient populations for which within-scan motion is frequently problematic. Aim 2 is to use a database of manually labeled datasets as the basis for the construction of an automated whole-brain segmentation procedure designed to assign a neuroanatomical label to every voxel in the brain (e.g. thalamus, caudate, putamen, etc). The segmentation procedure will disambiguate structures with similar tissue properties based on their location within the brain, as well as their spatial relationships to neighboring structures, encoded using an anisotropic markov random field. It is important to note that basing the segmentation upon the intrinsic tissue parameters renders the procedure largely insensitive to the details of a particular pulse sequence. Aim 3 is to employ the multi-spectral tissue parameters as the basis for surface-based morphometric analysis. The final aim is to validate the accuracy of the procedures, as well as their robustness to changes in scanner protocol. Upon completion of tool development they will be applied to the study of a variety of disorders, focusing on schizophrenia. Alzheimer's and Huntington's disease.